Zero-Shot 3D Shape Sketch View Similarity and Retrieval

We conduct a detailed study of the ability of pretrained on pretext tasks ViT and ResNet feature layers to quantify the similarity between pairs of 2D sketch views of individual 3D shapes. We assess the performance in terms of the models' abilities to retrieve similar views and ground-truth 3D shapes. Going beyond naive zero-shot performance study, we investigate alternative fine-tuning strategies on one or several shape classes, and their generalization to other shape classes. Leveraging progress in NPR (Non-Photo Realistic) rendering, we generate synthetic sketch views in several styles which we use to fine-tune pretrained foundation models using contrastive learning. We study how the scale of an object in a sketch affects the similarity of features at different network layers. We observe that depending on the scale, different feature layers can be more indicative of shape similarities in sketch views. However, we find that similar object scales result in the best performance of ViT and ResNet. In summary, we show that careful selection of a fine-tuning strategy allows us to obtain consistent improvement in zero-shot shape retrieval accuracy. We believe that our work will have a significant impact on research in the sketch domain, providing insights and guidance on how to adopt large pretrained models as perceptual losses.